7 papers
PAC to the Future: Zero-Knowledge Proofs of PAC Private Systems
Guilhem Repetto, Nojan Sheybani, Gabrielle De Micheli +1
Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a n…
Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
Yaman Jandali, Ruisi Zhang, Nojan Sheybani +1
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoptio…
Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications
Nojan Sheybani, Tengkai Gong, Anees Ahmed +3
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, tradi…
ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer +4
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a…
Zero-Knowledge Proof Frameworks: A Systematic Survey
Nojan Sheybani, Anees Ahmed, Michel Kinsy +1
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret…
Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
Ruisi Zhang, Neusha Javidnia, Nojan Sheybani +1
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations a…